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Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics.

René BreuerManuel MattheisenJosef FrankBertram KrummJens TreutleinLayla KassemJana StrohmaierStefan HermsThomas W MühleisenFranziska DegenhardtSven CichonMarkus M NöthenGeorge KarypisJohn KelsoeTiffany GreenwoodCaroline NievergeltPaul ShillingTatyana ShekhtmanHoward EdenbergDavid CraigSzabolcs SzelingerJohn NurnbergerElliot GershonNey Alliey-RodriguezPeter ZandiFernando GoesNicholas SchorkErin SmithDaniel KollerPeng ZhangJudith BadnerWade BerrettiniCinnamon BlossWilliam ByerleyWilliam CoryellTatiana ForoudYirin GuoMaria HipolitoBrendan KeatingWilliam LawsonChunyu LiuPamela MahonMelvin McInnisSarah MurrayEvaristus NwuliaJames PotashJohn RiceWilliam ScheftnerSebastian ZöllnerFrancis J McMahonMarcella RietschelThomas G Schulze
Published in: International journal of bipolar disorders (2018)
Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.
Keyphrases
  • genome wide
  • bipolar disorder
  • small molecule
  • copy number
  • dna methylation
  • high throughput
  • major depressive disorder
  • big data
  • health insurance
  • artificial intelligence
  • deep learning